Modeling method, device and storage medium of nonlinear model

Abstract

A modeling method, device and storage medium for nonlinear models. The method includes: determining the complete design point data for each target nonlinear basic process of each device; using the ratio of the similarity number supported by the similarity criterion to the similarity number based on the design point data, establishing a description formula for the nonlinear basic process, and obtaining a general model of the nonlinear basic process; the general model includes variable parameters that change nonlinearly with the change of actual operating parameters; constructing a machine learning algorithm between the actual operating parameters and the variable parameters, and establishing an association relationship between the machine learning algorithm and the general model; the general model of all target nonlinear general processes of each device and its associated machine learning algorithm constitute the general model of the device. This method can realize the modeling of nonlinear models for some models including nonlinear basic processes.

Description

Technical Field

[0001] The present invention relates to the industrial field, and in particular to a modeling method, a device and a computer-readable storage medium for a nonlinear model in an integrated energy system. Background Art

[0002] Distributed energy systems (DES) are considered to be one of the effective ways to solve the unstable renewable energy consumption. DES are being built all over the world, including my country, and the demand for models used for operation optimization and the arrangement of energy production and use of the overall system is also increasing. Previous researchers have developed some specific operation optimization models, which are only applicable to systems with specific energy sources and components. Therefore, their models cannot meet the requirements of the new thing of integrated energy services in actual implementation.

[0003] Therefore, it is necessary to provide a universal integrated energy system to make full use of various resource forms such as renewable energy, fossil fuels, residual heat and pressure, and new energy, so that they can cooperate with each other, establish innovative business mechanisms through the flexible operation of sources, networks, loads, and storage, and adopt intelligent means to achieve high-quality, high-efficiency, most economical and environmentally friendly regional electricity, heat, cold, gas and other loads. The integrated supply meets the requirements of random changes in terminal loads. Integrated energy promotes the absorption capacity of renewable energy and improves the comprehensive utilization rate of energy.

[0004] However, in the process of realizing the above-mentioned integrated energy system, it is necessary to establish models of many devices, but these devices usually include many nonlinear physical processes (also called basic processes), such as the relationship between flow and pressure in the compressor of the gas turbine, the process of converting mechanical energy into pressure energy, etc. Therefore, the model of such equipment is usually a nonlinear model, and how to establish these nonlinear models has become a problem that urgently needs to be solved. Summary of the invention

[0005] In view of this, the embodiments of the present invention propose, on the one hand, a modeling method of a nonlinear model, and on the other hand, propose a modeling device of a nonlinear model and a computer-readable storage medium, which are used to realize the modeling of nonlinear models for some models including nonlinear basic processes, and then used for the platform construction of an integrated energy system.

[0006] A modeling method for a nonlinear model proposed in an embodiment of the present invention includes: determining complete design point data for each target nonlinear basic process of each type of equipment; establishing a description formula for the nonlinear basic process by using the ratio of a similarity number supported by a similarity criterion to a similarity number based on the design point data, and obtaining a general model of the nonlinear basic process; the general model includes variable parameters that change nonlinearly with changes in actual operating parameters; constructing a machine learning algorithm between the actual operating parameters and the variable parameters, and establishing an association relationship between the machine learning algorithm and the general model; the general model of all target nonlinear general processes of each type of equipment and its associated machine learning algorithm constitute the general model of the equipment.

[0007] In one embodiment, it further includes: for each target nonlinear basis process of a specific device of the type of device, obtaining historical data of actual operating parameters and variable parameters corresponding to the target nonlinear basis process of the specific device, using the historical data to train the machine learning algorithm to obtain a variable parameter training model of the target nonlinear basis process; substituting the variable parameter training model of the target nonlinear basis process into the general model of the target nonlinear basis process to obtain a trained model of the target nonlinear basis process of the specific device; the trained models of all target nonlinear basis processes of the specific device constitute the trained model of the specific device.

[0008] In one embodiment, the variable parameter has a preset default value.

[0009] In one embodiment, the equipment includes: gas turbine, heat pump, internal combustion engine, steam turbine, waste heat boiler, absorption refrigerator, heating machine, multi-effect evaporator, water electrolysis hydrogen production, hydrogen production chemical equipment, reverse osmosis, fuel cell, boiler: the target nonlinear basic process of each device includes one or more of the following processes: heat transfer process, process of converting electrical energy into thermal energy, chemical process of separating solution substances using high-temperature thermal energy, electrochemical process, pipeline resistance process, flow and pressure related process, thermal energy into mechanical energy process, electrical energy into cooling, thermal energy process, distillation process, evaporation process and filtration process.

[0010] A modeling device for a nonlinear model proposed in an embodiment of the present invention includes: a determination module, used to determine the complete design point data for each target nonlinear basic process of each type of equipment; an establishment module, used to establish a description formula for the nonlinear basic process by adopting the ratio of the similarity number supported by the similarity criterion to the similarity number based on the design point data, and obtain a general model of the nonlinear basic process; the general model includes variable parameters that change nonlinearly with changes in actual operating parameters; a machine learning module, which constructs a machine learning algorithm between the actual operating parameters and the variable parameters, and establishes an association relationship between the machine learning algorithm and the general model; an encapsulation module, which is used to encapsulate the general model of all target nonlinear general processes of each type of equipment and its associated machine learning algorithm into a general model of this type of equipment.

[0011] In one embodiment, it further includes: a training module, which is used to obtain historical data of actual operating parameters and variable parameters corresponding to the target nonlinear basic process of a specific device of the device, and use the historical data to train the machine learning algorithm to obtain a variable parameter training model of the target nonlinear basic process; and a substitution module, which inputs the variable parameter training model of the target nonlinear basic process into the general model of the target nonlinear basic process to obtain a trained model of the target nonlinear basic process of the specific device; the trained models of all target nonlinear basic processes of the specific device constitute the trained model of the specific device.

[0012] In one embodiment, the variable parameter has a preset default value.

[0013] In one embodiment, the equipment includes: gas turbine, heat pump, internal combustion engine, steam turbine, waste heat boiler, absorption refrigerator, heating machine, multi-effect evaporator, water electrolysis hydrogen production, hydrogen production chemical equipment, reverse osmosis, fuel cell, boiler; the target nonlinear basic process of each device includes one or more of the following processes: heat transfer process, process of converting electrical energy into thermal energy, chemical process of separating solution substances using high-temperature thermal energy, electrochemical process, pipeline resistance process, process related to flow and pressure, process of converting thermal energy into mechanical energy, converting electrical energy into cooling, thermal energy process, distillation process, evaporation process and filtration process.

[0014] A modeling device for a nonlinear model proposed in an embodiment of the present invention includes: at least one memory and at least one processor, wherein: the at least one memory is used to store a computer program; the at least one processor is used to call the computer program stored in the at least one memory to execute the modeling method of the nonlinear model described in any of the above embodiments.

[0015] The computer-readable storage medium proposed in the embodiment of the present invention stores a computer program thereon; the computer program can be executed by a processor and implements the modeling method of the nonlinear model as described in any of the above embodiments.

[0016] It can be seen from the above scheme that, in the embodiment of the present invention, for each target nonlinear basic process of each device, the ratio of the similarity number supported by the similarity criterion to the similarity number based on the design point data is used to establish the description formula of the nonlinear basic process, and obtain the general model of the nonlinear basic process, so that the model can be used as a general model and applicable to a class of equipment. In addition, the general model includes variable parameters that change nonlinearly with the change of actual operating parameters, and by constructing a machine learning algorithm between the actual operating parameters and the variable parameters, the variable parameters can be obtained through machine learning, thereby making the general model have self-learning ability.

[0017] In addition, for a specific device of this type of equipment, by obtaining historical data of actual operating parameters and variable parameters corresponding to the target nonlinear basic mechanical process of the specific device for each of its target nonlinear basic mechanical processes, and using the historical data to train the machine learning algorithm to obtain a variable parameter training model of the target nonlinear basic mechanical process, and then substituting the variable parameter training model of the target nonlinear basic mechanical process into the general model of the target nonlinear basic mechanical process, the trained model of the target nonlinear basic mechanical process of the specific device can be obtained, that is, an instantiated model that conforms to the characteristics of the specific device.

[0018] Furthermore, by presetting default values ​​for the variable parameters, the nonlinear model can be made available when the variable parameters are not available for training on site, for example, when there is insufficient historical data.

[0019] Finally, the modeling method in the embodiment of the present invention can be applied to various nonlinear processes of various devices, which is not only convenient to implement but also has high accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings so that the above and other features and advantages of the present invention will be more apparent to those skilled in the art. In the accompanying drawings:

[0021] Figure 1 The figure is an exemplary flow chart of a modeling method of a nonlinear model in an embodiment of the present invention.

[0022] Figure 2 The figure is an exemplary flow chart of another nonlinear model modeling method in an embodiment of the present invention.

[0023] Figure 3It is an exemplary structural diagram of a modeling device of a nonlinear model in an embodiment of the present invention.

[0024] Figure 4 This is an exemplary structural diagram of a modeling device for another nonlinear model in an embodiment of the present invention.

[0025] The reference numerals are as follows:

[0026] Label meaning 101-104，201-203 step 301 Determine the module 302 Building Modules 303 Machine Learning Module 304 Encapsulation Module 305 Training Module 306 Substitute module 41 Memory 42 processor 43 bus DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is further described in detail with reference to the following embodiments.

[0028] Figure 1 FIG. 1 is an exemplary flow chart of a modeling method of a nonlinear model in an embodiment of the present invention. Figure 1 As shown, the method may include the following steps:

[0029] Step 101, for each target nonlinear basic process of each equipment, determine its complete design point data.

[0030] In this step, the basic process can sometimes be called a physical process, such as a heat transfer process, an electric energy conversion process, and the aforementioned flow and pressure related processes. For each device, the basic process of interest, that is, the basic process that needs to be modeled, can be determined. These basic processes that need to be modeled are called target basic processes, and nonlinear target basic processes can be called target nonlinear basic processes. For example, for a gas turbine, its target nonlinear basic process may include: the flow and pressure related process in the expansion turbine, the process of energy conversion between thermal energy and mechanical energy, etc.; for a heat pump, its target nonlinear basic process may include: heat transfer process, the process of converting electric energy into thermal energy, the chemical process of using high-temperature thermal energy to separate solution substances, electrochemical process, pipeline resistance process, flow and pressure related processes, etc. In addition, for internal combustion engines, steam turbines, waste heat boilers, absorption refrigerators, heating machines, multi-effect evaporators, water electrolysis to produce hydrogen, hydrogen to produce chemicals equipment, reverse osmosis, fuel cells, boilers and other equipment, the target nonlinear basic process of each device may include: flow and pressure related processes, thermal energy to mechanical energy processes, electrical energy to cold, thermal energy processes, pipeline resistance processes, heat transfer processes, distillation processes, evaporation processes, filtration processes, chemical reaction processes, electrochemical processes, etc. One or more of the following.

[0031] For each nonlinear basic process, the complete design point data can be restored based on the basic publicly published design parameters and the general design point information provided by the manufacturer to the user. For example, for the general model of the process related to flow and pressure, the design point data may include pressure ratio, air flow, etc. Based on these design point data, the efficiency, inlet resistance, and air extraction volume and other related design parameters that are not available to the user can be derived.

[0032] Step 102, using the ratio of the similarity number supported by the similarity criterion to the similarity number based on the design point data, establish a description formula for the nonlinear basic process to obtain a general model of the nonlinear basic process; the general model includes variable parameters that change nonlinearly with changes in actual operating parameters.

[0033] The actual working condition parameters refer to the parameters of a specific device that change with the actual working condition parameters. For example, the size that changes due to mechanical wear over a long period of use, or the temperature that changes with the seasons, or the related parameters that change with different working conditions, etc. The variable parameters may have preset default values.

[0034] Since there may be different models for each device, for example, taking a compressor as an example, there may be compressors with different powers such as 5M, 50M, and 500M, so in order to establish a general model of the compressor, it is necessary to use the similarity number supported by the similarity criterion to replace the specific parameter value. For example, still taking the general model of the correlation process of flow and pressure as an example, the similarity number supported by the similarity criterion of flow, pressure, and power is used to replace the specific parameters. For example, the similarity criterion of flow can be shown as follows (1):

[0035]

[0036] Among them, G1 is flow rate, T1 is temperature, P1 is pressure, G0 is the flow rate of the corresponding design point, T0 is the temperature of the corresponding design point, and P0 is the pressure of the corresponding design point.

[0037] Accordingly, the general model of the correlation process between flow and pressure can be expressed as follows:

[0038]

[0039] Wherein, f() is a function, coefficients a and b are variable parameters that change nonlinearly with the actual operating parameters. In practical applications, a default value can also be set for the variable parameters a and b. IGV is the inlet guide vane angle.

[0040] Step 103: construct a machine learning algorithm between the actual operating parameters and the variable parameters.

[0041] In this step, a machine learning algorithm between the actual operating parameters and the variable parameters can be constructed based on a machine learning big data analysis method such as a neural intelligent network or a support vector machine.

[0042] Step 104: The general model of all target nonlinear basic processes of each type of equipment and its associated machine learning algorithm constitute a general model of the equipment.

[0043] It can be seen that through the above process, a nonlinear universal model of each device can be established. Based on these universal models, a comprehensive energy system platform can be constructed.

[0044] In actual applications, after purchasing the integrated energy system platform, users need to build their own integrated energy system. At this time, each general model needs to be associated with specific equipment on site, so the general model needs to be instantiated. Figure 2 As shown, the following steps are included:

[0045] Step 201, for each target nonlinear basic process of a specific device of the type of equipment, obtain the historical data of actual operating parameters and variable parameters corresponding to the target nonlinear basic process of the specific device, use the historical data to train the corresponding machine learning algorithm, and obtain a variable parameter training model of the target nonlinear basic process.

[0046] In this step, during the specific training, a set of historical data of actual operating parameters is used as input sample values, and the historical data of variable parameters corresponding to the set of historical data of actual operating parameters is used as output sample values. By using a large number of input sample values ​​and corresponding output sample values ​​to train the machine learning algorithm, a self-learning model of the variable parameters, also called a training model, can be obtained.

[0047] For example, still taking the above-mentioned flow and pressure correlation process as an example, the historical data of the actual operating parameters of the gas turbine on site and its corresponding historical data of variable parameters can be obtained to obtain the input and output sample sets, and the training model of the variable parameters a and b can be obtained after training.

[0048] Step 202: Substitute the variable parameter training model of the target nonlinear basis process into the general model of the target nonlinear basis process to obtain a trained model of the target nonlinear basis process of the specific device. The trained model is a self-learning model with learning ability.

[0049] In this step, the variable parameter training model of the target nonlinear basic process can be substituted into the general model of the target nonlinear basic process according to the association relationship between the machine learning algorithm and the general model.

[0050] For example, still taking the above-mentioned correlation process between flow and pressure as an example, by inputting the current training model of the variable parameters a and b into the above formula (2), a general model of the correlation process between flow and pressure of the compressor of the on-site gas turbine can be obtained.

[0051] Step 203: The trained models of all target nonlinear basis processes of the specific device constitute the trained model of the specific device.

[0052] In actual use, the input parameters of the trained model may include the input parameters required by the trained models of all target nonlinear basis processes.

[0053] The modeling method of the nonlinear model in the embodiment of the present invention is described in detail above, and the modeling device of the nonlinear model in the embodiment of the present invention is described in detail below. The modeling device of the nonlinear model in the embodiment of the present invention can be used to implement the modeling method of the nonlinear model in the embodiment of the present invention. For details not disclosed in the embodiment of the device of the present invention, please refer to the corresponding description in the embodiment of the method of the present invention, and no further description will be given here.

[0054] Figure 3 FIG. 4 is an exemplary structural diagram of a modeling device for a nonlinear model in an embodiment of the present invention. Figure 3 As shown, the device can be Figure 3 As shown in the solid line part, it includes: a determination module 301, an establishment module 302, a machine learning module 303 and a packaging module 304.

[0055] The determination module 301 is used to determine the complete design point data for each target nonlinear basic process of each device.

[0056] The equipment may include: gas turbines, heat pumps, internal combustion engines, steam turbines, waste heat boilers, absorption refrigerators, heating machines, multi-effect evaporators, water electrolysis to produce hydrogen, hydrogen to produce chemicals equipment, reverse osmosis, fuel cells, boilers, etc.: The target nonlinear basic process of each device may include: heat transfer process, process of converting electrical energy into thermal energy, chemical process of separating solution substances using high-temperature thermal energy, electrochemical process, pipeline resistance process, flow and pressure related process, thermal energy to mechanical energy process, electrical energy to cooling, thermal energy process, distillation process, evaporation process and filtration process, etc. One or more of the above.

[0057] The establishment module 302 is used to establish a description formula of the nonlinear basic process by using the ratio of the similarity number supported by the similarity criterion to the similarity number based on the design point data, and obtain a general model of the nonlinear basic process; the general model includes variable parameters that change nonlinearly with the change of actual working condition parameters. The variable parameters may have preset default values.

[0058] The machine learning module 303 constructs a machine learning algorithm between the actual operating parameters and the variable parameters, and establishes an association relationship between the machine learning algorithm and the general model.

[0059] The encapsulation module 304 is used to encapsulate the general model of all target nonlinear general processes of each type of equipment and its associated machine learning algorithm into a general model of the equipment.

[0060] When instantiating the above general model, the modeling device of the nonlinear model can be as follows: Figure 3 As shown in the dotted part, it further includes: a training module 305 and a substitution module 306.

[0061] Among them, the training module 305 is used to obtain the historical data of actual operating parameters and variable parameters corresponding to the target nonlinear basic mechanical process of a specific device of this type of equipment for each target nonlinear basic mechanical process of the specific device, and use the historical data to train the machine learning algorithm to obtain a variable parameter training model of the target nonlinear basic mechanical process.

[0062] The substitution module 306 inputs the variable parameter training model of the target nonlinear basis process into the universal model of the target nonlinear basis process to obtain the trained model of the target nonlinear basis process of the specific device. The trained models of all target nonlinear basis processes of the specific device constitute the trained model of the specific device.

[0063] Figure 4 FIG. 1 is a schematic diagram of a structure of a modeling device for a nonlinear model according to an embodiment of the present invention. Figure 4 As shown, the system may include: at least one memory 41 and at least one processor 42. In addition, some other components may be included, such as communication ports, etc. These components communicate via a bus 43.

[0064] Wherein: at least one memory 41 is used to store a computer program. In one embodiment, the computer program can be understood to include Figure 3 The various modules of the modeling device of the nonlinear model shown. In addition, at least one memory 41 can also store an operating system, etc. The operating system includes but is not limited to: Android operating system, Symbian operating system, Windows operating system, Linux operating system, etc.

[0065] At least one processor 42 is used to call the computer program stored in at least one memory 41 to execute the modeling method of the nonlinear model described in the embodiment of the present invention. The processor 42 can be a CPU, a processing unit / module, an ASIC, a logic module or a programmable gate array, etc. It can receive and send data through the communication port.

[0066] It should be noted that not all steps and modules in the above processes and structure diagrams are necessary, and some steps or modules can be ignored according to actual needs. The execution order of each step is not fixed and can be adjusted as needed. The division of each module is only for the convenience of describing the functional division adopted. In actual implementation, a module can be implemented by multiple modules, and the functions of multiple modules can also be implemented by the same module. These modules can be located in the same device or in different devices.

[0067] It is understood that the hardware modules in the above-mentioned embodiments can be implemented mechanically or electronically. For example, a hardware module may include a specially designed permanent circuit or logic device (such as a dedicated processor, such as an FPGA or ASIC) for completing a specific operation. The hardware module may also include a programmable logic device or circuit (such as a general-purpose processor or other programmable processor) temporarily configured by software for performing a specific operation. As for whether to implement the hardware module in a mechanical manner, or using a dedicated permanent circuit, or using a temporarily configured circuit (such as configured by software), it can be determined based on cost and time considerations.

[0068] In addition, a computer-readable storage medium is provided in the embodiment of the present invention, on which a computer program is stored, and the computer program can be executed by a processor and realize the modeling method of the nonlinear model described in the embodiment of the present invention. Specifically, a system or device equipped with a storage medium can be provided, on which a software program code realizing the function of any implementation method in the above-mentioned embodiment is stored, and the computer (or CPU or MPU) of the system or device reads out and executes the program code stored in the storage medium. In addition, the operating system etc. operated on the computer can also be made to complete part or all of the actual operations through instructions based on the program code. The program code read out from the storage medium can also be written to a memory provided in an expansion board inserted into the computer or to a memory provided in an expansion unit connected to the computer, and then the CPU etc. installed on the expansion board or expansion unit can be made to perform part and all of the actual operations based on the instructions of the program code, thereby realizing the function of any implementation method in the above-mentioned embodiment. The storage medium implementation method for providing the program code includes a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a non-volatile memory card and a ROM. Alternatively, the program code may be downloaded from a server computer via a communications network.

[0069] It can be seen from the above scheme that, in the embodiment of the present invention, for each target nonlinear basic process of each device, the ratio of the similarity number supported by the similarity criterion to the similarity number based on the design point data is used to establish the description formula of the nonlinear basic process, and obtain the general model of the nonlinear basic process, so that the model can be applied to a class of equipment. In addition, the general model includes variable parameters that change nonlinearly with the change of actual operating parameters, and by constructing a machine learning algorithm between the actual operating parameters and the variable parameters, the variable parameters can be obtained through machine learning, thereby making the model have self-learning ability.

[0070] In addition, for a specific device of this type of equipment, by obtaining historical data of actual operating parameters and variable parameters corresponding to the target nonlinear basic mechanical process of the specific device for each of its target nonlinear basic mechanical processes, and using the historical data to train the machine learning algorithm to obtain a variable parameter training model of the target nonlinear basic mechanical process, and then substituting the variable parameter training model of the target nonlinear basic mechanical process into the general model of the target nonlinear basic mechanical process, the trained model of the target nonlinear basic mechanical process of the specific device can be obtained, that is, an instantiated model that conforms to the characteristics of the specific device.

[0071] Furthermore, by presetting default values ​​for the variable parameters, the nonlinear model can be made available when the variable parameters are not available for training on site, for example, when there is insufficient historical data.

[0072] Finally, the modeling method in the embodiment of the present invention can be applied to various nonlinear processes of various devices, which is not only convenient to implement but also has high accuracy.

[0073] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A modeling method for a nonlinear model, characterized in that: include: For each target nonlinear basic process of each equipment, determine the complete design point data (101); A description formula of the nonlinear basic process is established by using a ratio of a similarity number supported by a similarity criterion to a similarity number based on design point data, thereby obtaining a general model of the nonlinear basic process; the general model includes variable parameters (102) that change nonlinearly with changes in actual operating parameters, and the variable parameters include a coefficient of an inlet guide vane angle and a coefficient of a similar speed ratio; Constructing a machine learning algorithm between the actual operating condition parameters and the variable parameters, and establishing an association relationship between the machine learning algorithm and the general model (103); The general model of all target nonlinear general processes of each device and its associated machine learning algorithm constitute the general model of the device (104); Further including: For each target nonlinear basic process of a specific device of the device, obtain historical data of actual operating parameters and variable parameters corresponding to the target nonlinear basic process of the specific device, train the machine learning algorithm using the historical data, and obtain a variable parameter training model of the target nonlinear basic process (201); Substituting the variable parameter training model of the target nonlinear basis process into the general model of the target nonlinear basis process to obtain a trained model of the target nonlinear basis process of the specific device (202); The trained models of all target nonlinear basis processes of the specific device constitute the trained model (203) of the specific device.

2. The modeling method of the nonlinear model according to claim 1, characterized in that: The variable parameters have preset default values.

3. The method for modeling a nonlinear model according to claim 1 or 2, characterized in that: The equipment includes: gas turbines, heat pumps, internal combustion engines, steam turbines, waste heat boilers, absorption refrigerators, heating machines, multiple-effect evaporators, water electrolysis hydrogen production, hydrogen production chemical equipment, reverse osmosis, fuel cells, boilers; The target nonlinear basis process of each device includes one or more of the following processes: Heat transfer processes, processes that convert electrical energy into thermal energy, chemical processes that use high temperature thermal energy to separate solution substances, electrochemical processes, pipeline resistance processes, processes related to flow and pressure, processes that convert thermal energy into mechanical energy, electrical energy into cooling, thermal energy processes, distillation processes, evaporation processes and filtration processes.

4. A modeling device for a nonlinear model, characterized in that: include: A determination module (301) is used to determine complete design point data for each target nonlinear basic process of each device; An establishment module (302) is used to establish a description formula of the nonlinear basic process by using the ratio of the similarity number supported by the similarity criterion to the similarity number based on the design point data, and obtain a general model of the nonlinear basic process; the general model includes variable parameters that change nonlinearly with the change of actual operating parameters, and the variable parameters include a coefficient of the inlet variable guide vane angle and a coefficient of the similar speed ratio; A machine learning module (303) constructs a machine learning algorithm between the actual operating condition parameters and the variable parameters, and establishes an association relationship between the machine learning algorithm and the general model; A packaging module (304) is used to package the general models of all target nonlinear general processes of each type of equipment and the associated machine learning algorithms into a general model of the equipment; Further including: A training module (305) is used to obtain, for each target nonlinear basic process of a specific device of the device, historical data of actual operating parameters and variable parameters corresponding to the target nonlinear basic process of the specific device, and train the machine learning algorithm using the historical data to obtain a variable parameter training model of the target nonlinear basic process; Substitution module (306) inputs the variable parameter training model of the target nonlinear basis process into the general model of the target nonlinear basis process to obtain the trained model of the target nonlinear basis process of the specific device; the trained models of all target nonlinear basis processes of the specific device constitute the trained model of the specific device.

5. The nonlinear model modeling device according to claim 4, characterized in that: The variable parameters have preset default values.

6. The nonlinear model modeling device according to claim 4 or 5, characterized in that: The equipment includes: gas turbine, heat pump, internal combustion engine, steam turbine, waste heat boiler, absorption refrigerator, heating machine, multi-effect evaporator, water electrolysis hydrogen production, hydrogen production chemical equipment, reverse osmosis, fuel cell, boiler: The target nonlinear basic process of each device includes one or more of the following processes: heat transfer process, process of converting electrical energy into thermal energy, chemical process of separating solution substances using high-temperature thermal energy, electrochemical process, pipeline resistance process, flow and pressure related process, thermal energy into mechanical energy process, electrical energy into cooling, thermal energy process, distillation process, evaporation process and filtration process.

7. A modeling device for a nonlinear model, characterized in that: include: At least one memory (41) and at least one processor (42), wherein: The at least one memory (41) is used to store a computer program; The at least one processor (42) is used to call the computer program stored in the at least one memory (41) to execute the modeling method of the nonlinear model according to any one of claims 1 to 3.

8. A computer-readable storage medium having a computer program stored thereon; characterized in that: The computer program can be executed by a processor and implements the nonlinear model modeling method according to any one of claims 1 to 3.

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